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How Aviz and Endace Strengthen Network Forensics with Continuous Packet Evidence

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To investigate cyberattacks and suspicious activity - and to meet compliance regulations - security teams rely on evidence found within packets. However, traffic is traversing not only across their data centers, but also cloud platforms, campuses, and edges, making it challenging to capture every essential packet. Some typical problems that teams encounter are: * Overloaded packet capture systems unable to handle traffic spikes. * Irrelevant data obscuring important evidence. * Inconsistent historical records creating drag on investigations. * Higher infrastructure costs, without an increase in visibility. The Aviz Networks-Endace integrated solution makes this task simpler by guaranteeing that only optimized, high-fidelity traffic arrives at packet capture systems. Aviz Deep Network Observability (DNO) smart services efficiently aggregate, filter, dedup, and normalize traffic before directing it to Endace Always-On Packet Capture, which ingests, indexes, and permanently stores the tra...
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Why AI Factory Networking Should Be Validated Before Deployment

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Assembling AI infrastructure is significantly different than just installing servers and switches. Modern AI factories are highly interconnected and need to integrate networking, GPUs, storage, orchestration, and suites of applications. If configuration or integration issues appear in the deployed system, there will likely be major interruptions in the operation and costs associated with delays. Many companies struggle with: * A shortage of real-world physical lab testing. * Long validation/deployment cycles. * Discovering integration bugs at an inconvenient (read, costly!) late stage. * The hidden post-deployment rework needed to fix. That's where Aviz ONES on NVIDIA DSX Air helps deliver a better AI infrastructure build process: digital twin simulation. The idea is simple: instead of building, and then testing, why not design, simulate, and validate your entire AI networking environment beforehand? Engineers can create a model of their production-scale network, simulate traffic f...
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Why Aviz Service Node v2.4 Is the Missing Intelligence Layer for Modern Networks

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In today's era, enterprise and telecom networks span much wider than a data center. These networks' traffic traverse through the cloud, edge, hybrid infrastructure, and wireless networks, giving network administrators a challenging time in gaining end-to-end visibility. Lack of smart traffic analysis has put security and network teams' fragmented views and increased incident response times. Typical issues faced are: The inability of getting visibility across a distributed environment. Replication of traffic which impacts data processing costs. Cost-intensive hardware appliances tied to proprietary technology. Lack of data to feed the AI-based observability platforms. Aviz Service Node v2.4 processes raw packet traffic to create machine-readable, application-aware metadata which is consumable by present-day observability platforms. ASN, through deep packet inspection (DPI) and application classification, enhances packet-aware traffic data with context so that AI/ ML can eff...
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Why AI Factory Design Needs Real-Time Validation Instead of Physical Labs

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Modern AI infrastructure no longer only means networking together a stack of GPUs with a bunch of switches. The modern AI factory now relies on tightly optimized networking in which a slight configuration miss will affect performance, price or deployment timeline. Traditionally, this means it requires setting up and testing the network configuration in a physical lab which is expensive, time consuming and not easily scalable. Why, you ask? Because hardware testing alone can be cost prohibitive. Design cycles are serial and iterative, meaning designs and validation are split into distinct phases that take weeks or days to cycle through. When you're designing for the future, you have configuration misses which aren’t caught until you’ve rolled out production. It’s time to disrupt the cycle with digital twins. What NVIDIA AIR and Aviz ONES give you NVIDIA AIR provides an all-digital, virtualized AI network simulation where you can simulate all the switches, links, automation and confi...
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Can Healthcare Stop Ransomware Faster Without Better Network Visibility?

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Many hackers are able to breach various parts of the healthcare system because the SecOps team is unable to quickly understand how far an attacker can spread throughout multiple systems. Hospitals, clinics, telemedicine, cloud services, devices and facility systems create traffic on a common network; however, due to lack of central control over how traffic flows & devices in the Silverado system, many of these facilities have blind spots pertaining to the analysis of East to West traffic, devices that are not monitored (unmanaged) & mixed infrastructure (devices that do not have agent-based security monitoring). Some examples of this are: Almost all types of ransomware operated by transferring themselves around to other locations before they perform file&folder encryptions. A lot of the different device types and workloads out there within the Healthcare ecosystem will not be able to utilize endpoint agents. Packet level evidence will assist with identifying unusual flows o...
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Can Healthcare Device Security Improve Without Starting at the Network Traffic Layer?

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In today's world of healthcare, thousands of connected devices rely on one another, such as infusion pumps, imaging equipment, badge readers, controllers of H/VAC, clinical stations and tele-health endpoints. A majority of these types of devices do not support security agents, can't be patched and were not designed with cybersecurity in mind; they were simply designed to provide safe reliable clinical care.  Here are some real-world observations related to the above statement: • There are substantial amounts of valuable traffic signals that are generated on connected medial and facility devices • Many of the devices cannot be secured by the use of traditional endpoint controls • Packet-level visibility can provide insight into device behaviour patterns, communications, and risk • Optimized delivery of traffic helps improve the accuracy of asset discovery and monitoring tools • Network evidence can support security compliance and incident investigation processes There is a comm...
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Can AI Networks Be Operated Before They Reach Production?

AI factories have already introduced a completely new way of thinking regarding networking. Network used to serve the purpose of connecting devices. Nowadays, it influences the performance of workload execution, the level of isolation, usage of GPU, troubleshooting speeds, etc. It has become possible to validate the operational processes by creating a digital twin environment before any changes get implemented. By doing that, it would be possible to test AI fabrics, topology configuration options, create tenants, telemetry gathering, and much more within the environment. It is important not to forget about switching from automated deployment and management to intelligent operations. It will be possible to analyze alarms, find reasons for network congestion, detect drifts in configuration, examine telemetry information, generate health summaries, and so on. In other words, there won't be such a need to use multiple dashboards and CLI configurations. To sum up, an AI-friendly network...
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